Industry increasingly depends upon highly automated data acquisition and control systems to ensure that industrial processes are run efficiently and reliably while lowering their overall production costs. Data acquisition begins when a number of sensors measure aspects of an industrial process and report their measurements back to a data collection and control system. Such measurements come in a wide variety of forms. By way of example the measurements produced by a sensor/recorder include: a temperature, a pressure, a pH, a mass/volume flow of material, a counter of items passing through a particular machine/process, a tallied inventory of packages waiting in a shipping line, cycle completions, etc. Often sophisticated process management and control software examines the incoming data associated with an industrial process, produces status reports and operation summaries, and, in many cases, responds to events/operator instructions by sending commands to actuators/controllers that modify operation of at least a portion of the industrial process. The data produced by the sensors also allow an operator to perform a number of supervisory tasks including: tailor the process (e.g., specify new set points) in response to varying external conditions (including costs of raw materials), detect an inefficient/non-optimal operating condition and/or impending equipment failure, and take remedial action such as move equipment into and out of service as required.
A very simple and familiar example of a data acquisition and control system is a thermostat-controlled home heating/air conditioning system. A thermometer measures a current temperature, the measurement is compared with a desired temperature range, and, if necessary, commands are sent to a furnace or cooling unit to achieve a desired temperature. Furthermore, a user can program/manually set the controller to have particular setpoint temperatures at certain time intervals of the day.
Typical industrial processes are substantially more complex than the above-described simple thermostat example. In fact, it is not unheard of to have thousands or even tens of thousands of sensors and control elements (e.g., valve actuators) monitoring/controlling all aspects of a multi-stage process within an industrial plant or monitoring units of output produced by a manufacturing operation. The amount of data sent for each measurement and the frequency of the measurements varies from sensor to sensor in a system. For accuracy and to facilitate quick notice/response of plant events/upset conditions, some of these sensors update/transmit their measurements several times every second. When multiplied by thousands of sensors/control elements, the volume of data generated by a plant's supervisory process control and plant information system can be very large.
Specialized process control and manufacturing/production information data storage facilities (also referred to as plant historians) have been developed to handle the potentially massive amounts time-series of process/production information generated by the aforementioned systems. An example of such system is the WONDERWARE HISTORIAN. A data acquisition service associated with the historian collects time-series data values for observed parameters from a variety of data sources (e.g., data access servers). The collected time-series data is thereafter deposited with the historian to achieve data access efficiency and querying benefits/capabilities of the historian's database. Through its database, the historian integrates plant data with event, summary, production and configuration information.
Information is retrieved from the tables of historians and displayed by a variety of historian database client applications including trending and analysis applications at a supervisory level of an industrial process control system/enterprise. Such applications include graphical displays for presenting/recreating the state of an industrial process or plant equipment at any particular point (or series of points) in time. A specific example of such client application is the WONDERWARE HISTORIAN CLIENT trending and analysis application. This trending and analysis application provides a flexible set of graphical display and analytical tools for accessing, visualizing and analyzing plant performance/status information provided in the form of streams of time-series data values for observed parameters.
Traditionally, plant databases, referred to as historians, have collected and stored in an organized manner (i.e., “tabled”), to facilitate efficient retrieval by a database server, streams of timestamped time-series data values for observed parameters representing process/plant/production status over the course of time. The status data is of value for purposes of maintaining a record of plant performance and presenting/recreating the state of a process or plant equipment at a particular point in time. Over the course of time, even in relatively simple systems, Terabytes of the streaming timestamped information are generated by the system and tabled by the historian.
In many instances, enterprises or the processes (e.g., a pipeline) within an enterprise are spread over vast geographic regions. For example, a company may operate multiple refineries or bottling plants at a number of distant geographic locations. It is generally desirable, in such systems, to place at least one historian (process database) at each geographic location. The information from the geographically distinct locations is provided to a parent historian operating at a hierarchical level (e.g., tier two) above the various geographically distinct (e.g., tier one) data historians. See, e.g., FIG. 1 (described herein below). Each of the “tier one” historians (100, 102, and 104) accumulates local information associated with a localized plant. A centralized “tier two” historian 110 receives and accumulates sets of historical data acquired and passed on by each of the “tier one” historians (100, 102, and 104). Though only a few historians are shown in FIG. 1, it will be understood by those skilled in the art that potentially dozens or even hundreds of historians can be provided at level one of a multi-tiered historian configuration.
As the size of enterprises increases, the amount of information generated and communicated over a network and stored in the historians of such enterprises increases. In very large enterprises, the total amount of data generated (communicated) by a set of independently operating process data “tier one” historians can overwhelm a centralized “tier two” recipient of such data. Moreover, the transmission of replicated data from the tier one historians to the centralized recipient can overwhelm the network used to communicate the replicated data.